SUMMARY OF WORK As a result of extensive collaboration with Clara Franzini-Armstrong we have obtained extensive statistical data on the distribution of organelles and ryanodine receptors in rabbit siono-atrial node cells. These data indicated that the parameters of our 3D stochastic SANC model need to be extensively revised. Since the last report we have found a way to do this so that the model continues to predict qualitative behavior consistent with observations. However, the EM data are not sufficient to define the critical distribution of ryanodine receptors on the cell surface. We have done extensive imaging using ultra-resolution SIM microscopy, and have developed software that enables 3D reconstruction of the location and size of ryanodine receptor clusters, which will be used directly in the model. Eventually, we hope to develop 3D coordinate systems based on the shape of individual cells. We have developed software that can detect, classify and track calcium release event in 3D+time, both in simulations and in experimental records. This has led to new understanding of the way that propagation occurs in the model as a function of adrenergic stimulation, and to the discovery that there are many more release events in experimental records than previously suspected. We have begun studies of heterogeneity of cells within the sinus node, both in isolated cells and in high space and time resolution images of whole sinus node preparations from mouse. This has led to the hypothesis that some pacemaker cells may be quiescent at rest and be recruited under conditions of adrenergic (fight-or-flight) stimulation. In the next program period we anticipate revising the modeling to consider human sinus node. As advised by the Board of Scientific Counselors, we are undertaking to translate our extensive modeling software into a form that can be used by other investigators. During this project period we have begun analyzing and attempting to simulate calcium signals from intact sinus node preparations. This new data has shown that the roles and interaction of different cells is different and more complicated that widely believed in the field. We have begun developing multi-cellular simulation models making use of the additional computing power available from biowulf this year. This modeling, together with data being obtained by SIM using machine-learning methods as suggested wholly novel hypotheses about the mechanism of cell-to-cell interaction which will be explored theoretically and experimentally in the next project period.